Friendship Recommendation System Using Topological Structure of Social Networks

Popularity and importance of Recommendation System is being increased day by day in both commercial and research community. Social networks (SNs) like Facebook, Twitter, and LinkedIn draw more attention since without any previous knowledge a lot of connections have been established. The creation of relationship between users is the key feature of a social network. Therefore, it is important for researchers to look for a new way to provide recommendations with more relevance. This paper proposes two algorithms for recommending a new friend in online social networks. The first algorithm is based on the number of mutual friends and second is based on influence score. These recommendation algorithms use collaborative filtering and provide the idea of doing recommendations (e.g., Facebook recommend friends, Netflix suggest movies, Amazon recommend products, etc.). Obtained results and analysis indicate that influence-based recommendation system is more accurate as compared to mutual friend-based recommendation. These proposed recommendation algorithms can be used for the development of an effective social networking or e-commerce site and thereby providing a better experience to users.